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Likelihood‐based analysis of outcome‐dependent sampling designs with longitudinal data
Author(s) -
Zelnick Leila R.,
Schildcrout Jonathan S.,
Heagerty Patrick J.
Publication year - 2018
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7633
Subject(s) - outcome (game theory) , estimator , statistics , computer science , sampling design , inference , sampling (signal processing) , univariate , random effects model , regression analysis , econometrics , stratified sampling , missing data , key (lock) , data mining , mathematics , medicine , artificial intelligence , multivariate statistics , meta analysis , population , environmental health , mathematical economics , filter (signal processing) , computer security , computer vision
The use of outcome‐dependent sampling with longitudinal data analysis has previously been shown to improve efficiency in the estimation of regression parameters. The motivating scenario is when outcome data exist for all cohort members but key exposure variables will be gathered only on a subset. Inference with outcome‐dependent sampling designs that also incorporates incomplete information from those individuals who did not have their exposure ascertained has been investigated for univariate but not longitudinal outcomes. Therefore, with a continuous longitudinal outcome, we explore the relative contributions of various sources of information toward the estimation of key regression parameters using a likelihood framework. We evaluate the efficiency gains that alternative estimators might offer over random sampling, and we offer insight into their relative merits in select practical scenarios. Finally, we illustrate the potential impact of design and analysis choices using data from the Cystic Fibrosis Foundation Patient Registry.